Techniques relating to delivery plan generation are improved. A delivery plan generation method comprises: generating a plurality of delivery patterns (S110); narrowing the plurality of delivery patterns down to a designated number of delivery patterns, based on a plurality of solutions calculated as a result of inputting the plurality of delivery patterns to an annealing machine (30) (S120); and generating a delivery plan based on delivery patterns selected from the designated number of delivery patterns (S130).

A non-transitory computer-readable storage medium storing a route search program that causes a computer to execute a process that includes acquiring a storage period of an item for each point of a plurality of points in a case where the item is moved from a departure point to a destination point, the plurality of points including the departure point, the destination point, and one or more relay points; and searching for a route along which the item is moved from the departure point to the destination point based on a traveling cost, storage information, and the acquired storage period, a traveling cost indicating a cost for moving the item between each two points of the plurality of points, the storage information indicating a storage coefficient being used for calculation of a storage cost of the item at each point of the plurality of points in accordance with the storage period.

The present disclosure generally relates to a computer implemented method for operating an autonomous vehicle, specifically in relation to efficient planning of interactions with service providers. The present disclosure also relates to a corresponding control system and computer program product.

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for selecting actions to be performed by an agent interacting with an environment. In one aspect, a method comprises, at each of one or more time steps: generating a respective action score for each action in a set of possible actions, wherein the set of possible actions comprises: (i) a plurality of atomistic actions, and (ii) one or more optimization actions, wherein each optimization action is associated with a respective objective function that measures performance of the agent on a corresponding auxiliary task; selecting an action from the set of possible actions in accordance with the action scores, wherein the selected action is an optimization action; in response to selecting the optimization action, performing a numerical optimization to identify a sequence of one or more atomistic actions that are predicted to optimize the objective function.

An information processing apparatus includes a controller, the controller being configured to select a first mobile body to deliver baggage to a delivery destination existing in a predetermined facility from among a plurality of types of delivery mobile bodies, wherein the controller selects the first mobile body, based on required time necessary to deliver the baggage to the delivery destination in a site of the predetermined facility, for each delivery mobile body.

In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network. The transfer system causes transferences of the corresponding quantities of the types of requested items to each of the plurality of the destination sites based on the integral approximate solution.

In implementations of item transfer control systems, a computing device implements a transfer system to receive input data describing types of requested items and corresponding quantities of the types of requested items to receive at each of a plurality of destination sites and types of available items and corresponding quantities of the types of available items that are available at each of a plurality of source sites. The transfer system constructs a flow network having a source node for each of the plurality of the source sites and a destination node for each of the plurality of the destination sites. An integral approximate solution is generated that transfers the corresponding quantities of the types of requested items to each of the plurality of the destination sites using a maximum flow solver and the flow network. The transfer system causes transferences of the corresponding quantities of the types of requested items to each of the plurality of the destination sites based on the integral approximate solution.

The present invention includes methods, circuits, devices, systems and associated computer executable code for providing driver decision making support. According to some embodiments, there may be provided a driver decision support system, which may generate action recommendations to a commercial driver, such as a taxi driver, a cab driver, a limo driver or any other kind of driver who picks up and transports passengers or cargo on an ad hoc (or otherwise flexible/uncertain) basis.

A system for determining and executing an autonomous-vehicle vehicle travel route, including a hardware-based processing unit and a non-transitory computer-readable storage medium. The storage medium includes an input-interface module that, when executed by the hardware-based processing unit, obtains factor data indicating factors relevant to determining a vehicle travel route. The storage medium also includes a route-generation module comprising a route-complexity sub-module. The route-complexity sub-module determines, based on the factor data, route-complexity indexes corresponding to respective optional routes. The route-generation module determines the vehicle travel route based on the route-complexity indexes. The storage in various embodiments includes other sub-modules associated with other elements, such as autonomous-driving safety, comfort, stress, pollution, scenery, or infrastructure-accessibility, for determining and executing an autonomous-driving travel route. In some embodiments, the storage includes an autonomous-driving perceptions module and an autonomous-driving control module for modifying vehicle functions in executing the autonomous-driving travel route.

A method for the optimized use of a parking area. Vehicles which are to be parked on the parking area are each assigned a parking space, the vehicles navigating to the respective assigned parking space, in particular autonomously. Furthermore, vehicles may carry out a change of the parking space in order to enable an improved use of the available parking area or a faster availability of the vehicle, whereby overall an optimized use of the parking area is achieved. Initially, an available range of the respective vehicles is ascertained, and the assignment of the respective parking space and/or a possible change of the parking space are made dependent on the available range of the individual vehicles. The available range of a vehicle is in particular described by a residual fuel amount or a charge state of the vehicle.